19 citations · 43 across the 9 of their papers we have counts for
6 papers · 1 filter
DHIL-GT: Scalable Graph Transformer with Decoupled Hierarchy Labeling
Ningyi Liao, Zihao Yu, Siqiang Luo
Graph Transformer (GT) has recently emerged as a promising neural network architecture for learning graph-structured data. However, its global attention mechanism with quadratic co…
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness
Ningyi Liao, Haoyu Liu, Zulun Zhu +2
With recent advancements in graph neural networks (GNNs), spectral GNNs have received increasing popularity by virtue of their ability to retrieve graph signals in the spectral dom…
Unifews: You Need Fewer Operations for Efficient Graph Neural Networks
Ningyi Liao, Zihao Yu, Ruixiao Zeng +1
Graph Neural Networks (GNNs) have shown promising performance, but at the cost of resource-intensive operations on graph-scale matrices. To reduce computational overhead, previous…
SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation
Haoyu Liu, Ningyi Liao, Siqiang Luo
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their…
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
Ningyi Liao, Dingheng Mo, Siqiang Luo +2
Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in sol…
Achieving Adversarial Robustness via Sparsity
Shufan Wang, Ningyi Liao, Liyao Xiang +2
Network pruning has been known to produce compact models without much accuracy degradation. However, how the pruning process affects a network's robustness and the working mechanis…